Abstract
Parkinson’s disease is a long-term degenerative disorder of the nervous system which mainly affects the motor system. Recent studies have shown that rhythmic auditory stimulation (RAS) improves gait performance in PD patients [1]; they achieve improved walking speeds, step lengths and step cadence [2, 3]. Home based solutions for monitoring the disease are being developed, as various cuing devices using smart phone based Apps for RAS [2, 4, 5, 6, 7] or using an RAS App as a solution when freezing of gait is detected [8].
Currently, none of these solutions provide real-time analysis of the RAS with respect to temporal gait parameters nor feedback about performance to the patient. This could be achieved by adding sensors to the App to allow monitoring of RAS and its effect and to give feedback on the quality and quantity of the exercises, with the additional benefit of being able to provide gait analysis.
This thesis aims to fill this gap by implementing standard gait analysis methods into an existing RAS App (HOOP) and proposing a robust algorithm for the assessment of the RAS with respect to gait. Furthermore, this algorithm will be used to allow the RAS to be adapted to the users’ speed or to enable a goal orientated RAS. This is the first step towards assessing the clinical relevance of a home-based RAS system